98%
921
2 minutes
20
Mutations in the human ABCC6 gene cause pseudoxanthoma elasticum (PXE), a hereditary disorder that impacts the skin, eyes, and cardiovascular system. Currently, the diagnosis of PXE is based on physical findings and histological examination of a biopsy of affected skin. We have combined two simple, polymerase chain reaction (PCR)-based methods to develop a rapid, reliable genetic assay for the majority of known PXE mutations. After PCR amplification and heteroduplex formation, mutations in exon 24 and exon 28 of the ABCC6 gene were detected with Surveyor nuclease, which cleaves double-stranded DNA at any mismatch site. Mutations originating from deletion of a segment of the ABCC6 gene between exon 23 and exon 29 (ex23_ex29del) were detected by long-range PCR. Size analysis of digestion fragments and long-range PCR products was performed by agarose gel electrophoresis. The methods accurately identified mutations or the absence thereof in 16 affected individuals as confirmed by DNA sequencing. Fifteen patients had one or two point mutations, and two of these individuals carried the ex23_ex29del in their second allele. This mutation detection and mapping strategy provides a simple and reliable genetic assay to assist in diagnosis of PXE, differential diagnosis of PXE-like conditions, and study of PXE genetics.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1867419 | PMC |
http://dx.doi.org/10.2353/jmoldx.2007.060093 | DOI Listing |
Med Sci (Paris)
September 2025
CIRI, Centre international de recherche en infectiologie Université de Lyon, Inserm U1111, Université Claude Bernard Lyon 1, CNRS UMR5308, ENS de Lyon, Lyon, France.
The accumulated knowledge on the biology of the HIV-1 virus has led to the emergence of technologies that exploit the architecture of retroviruses and their integration or vectorization properties. This field of study constitutes retroviral vectorology, democratized in laboratories by the use of lentiviral vectors. By hijacking retroviral assembly, other systems are emerging and are increasingly mentioned in recent literature.
View Article and Find Full Text PDFJ Infect Dev Ctries
August 2025
Department of Medical Microbiology, Faculty of Medicine, Ege University, Izmir 35100, Turkey.
Introduction: The aim of this study was to compare the performance of different clinical specimens-nasopharyngeal (NP) swabs collected by healthcare professionals (HCP-NP), self-collected nasal swabs (Sc-N), and saliva samples (S)-in diagnostic tests for investigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA and influenza A/B RNA.
Methodology: These clinical samples were collected from 404 symptomatic cases and tested with the SARS-CoV-2 and influenza A/B RNA tests on the cobas 6800 System of Roche Molecular Systems (Roche Molecular Systems, Pleasanton, USA). The SARS-CoV-2 or influenza virus infection status was determined for all patients based on the predefined criteria and corresponding algorithms.
J Neurol
September 2025
Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Background: The "Systematic Screening of Handwriting Difficulties in Parkinson's Disease" (SOS) test is the only tool specifically designed to evaluate handwriting in people with Parkinson's Disease (pwPD). It is language specific.
Objective: To assess the construct validity, intrarater and interrater reliability of the Italian version of the SOS test.
mSystems
September 2025
Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
Genome-scale metabolic models (GEMs) are widely used in systems biology to investigate metabolism and predict perturbation responses. Automatic GEM reconstruction tools generate GEMs with different properties and predictive capacities for the same organism. Since different models can excel at different tasks, combining them can increase metabolic network certainty and enhance model performance.
View Article and Find Full Text PDFBrief Bioinform
August 2025
Department of Respiratory Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an 710004, China.
Accurate tumor mutation burden (TMB) quantification is critical for immunotherapy stratification, yet remains challenging due to variability across sequencing platforms, tumor heterogeneity, and variant calling pipelines. Here, we introduce TMBquant, an explainable AI-powered caller designed to optimize TMB estimation through dynamic feature selection, ensemble learning, and automated strategy adaptation. Built upon the H2O AutoML framework, TMBquant integrates variant features, minimizes classification errors, and enhances both accuracy and stability across diverse datasets.
View Article and Find Full Text PDF